Presently, voice recognition systems are becoming popular with consumers of conventional computers due to the availability of continuous speech recognition systems. These applications are generally tailored for speaker-dependent voice recognition. That is, to provide a high degree of accuracy in the conversion of voice to a textual message, the continuous speech recognition system must be trained by a particular speaker's voice. This is generally performed by having the speaker read a canned message of several paragraphs, which is then recorded and analyzed by the speech recognition system to develop a set of statistical models of the speaker's voice. These models are later used by the continuous speech recognition system to convert the speaker's voice signals into a textual message.
Although the present approach provides a relatively high degree of accuracy in the process of converting voice to a textual message, a need for higher degrees of accuracy approaching a flawless conversion is desirable. Present continuous speech recognition systems suffer several disadvantages in reaching a flawless conversion standard. For example, present algorithms rely heavily on the spectral envelope features of the analyzed speech signals to generate a textual message equivalent. This is disadvantageous because such a method fails to account for other features in speech such as the shape of the speech spectrum, which may be helpful in improving the accuracy of voice conversion. Additionally, present algorithms are not well adapted to recognize speech at a high degree of accuracy from speakers who have not trained the system with their particular voice characteristics.
Because of the foregoing limitations in prior art voice recognition systems, service providers of radio communication systems have opted to utilize human operators to transcribe voice messages to text messages from callers who intend to send messages to one or more SCRs (selective call radios) of the radio communication system. Service providers are generally hesitant in using a completely automated voice recognition system, because present voice recognition systems cannot guarantee flawless conversion of voice messages to text messages. The use of human operators, however, is expensive, especially for radio communication systems that operate 24 hours a day, every day of the year. Consequently, a need exists for automating the conversion of voice messages to text messages in a radio communication system to the extent that reliance on human operators to perform this conversion is either eliminated or substantially reduced.
Accordingly, what is needed is an apparatus and method for reliable conversion of voice in a radio communication system that satisfies present needs, and overcomes the foregoing disadvantages in the prior art.